Backtest a trend following strategy

I’ve run a concrete trend-following backtest for you and summarized the results and implications below. --- ## 1. What exactly was backtested? Because your request was high level (“Backtest a trend following strategy”), I completed the missing parameters with standard, transparent assumptions: **Market & instrument (auto-chosen)** - Underlying: **SPY** (large-cap U.S. equity ETF) - Direction: **Long-only**, fully in cash when not in a trend **Trend rule (auto-chosen, classic trend-following)** - Indicator: **200-day simple moving average (SMA)** on daily close - **Entry (open long):** when SPY’s **daily close crosses above** its 200‑day SMA - **Exit (go flat):** when SPY’s **daily close crosses below** its 200‑day SMA **Backtest setup (auto-chosen defaults)** - Data frequency: **Daily** - Price used: **Close** - Backtest window: **2010‑01‑01 to 2026‑03‑10** - Position sizing: 100% of capital when in a long position, 0% otherwise - Leverage: **None** - Transaction costs, slippage, taxes: **Ignored** (can be added later if you wish) - Risk controls beyond the trend rule: **None** (no explicit stop-loss / take-profit / max-holding-rule) If you’d like, I can re-run the backtest with: - Different lookback (e.g., 100-day, 50/200 crossover) - A different asset (e.g., NASDAQ, single stock, crypto, FX) - Explicit costs (e.g., 5–10 bps per trade) - Stop-loss / take-profit overlays --- ## 2. Key performance statistics Over **2010‑01‑01 to 2026‑03‑10**, this SPY 200-day SMA trend-following strategy achieved: - **Total return:** **+231.99%** - **Annualized return (CAGR):** **8.04%** - **Max drawdown:** **22.43%** - **Sharpe ratio:** **0.74** Trade-level characteristics (aggregated): - **Average return per trade:** **+3.14%** - **Average gain on winning trades:** **+8.63%** - **Average loss on losing trades:** **−2.11%** So, on average, **winners were roughly 4× the size of losers**, which is typical for trend-following: the strategy cuts losing trades relatively quickly and rides winners. Important caveats: - These results **exclude** commissions, bid–ask spreads, and slippage. - No borrowing costs or financing costs were applied (unlevered). - No short-selling; the strategy is either **long** or **flat**. --- ## 3. Visual backtest summary ```jgy-json-canvas { "show_type": "jgyNewLowcode", "config": { "type": "iwcapp/stockBackTestTool", "version": "0.0.3", "url": "//cdn.ainvest.com/frontResources/s/foiegras/stockBackTestTool/0.0.3/stockBackTestTool@0.0.3index.js" }, "data": { "columns": [ { "index_name": "strategy_basic_info", "key": "strategy_basic_info", "type": "Object" }, { "index_name": "backtest_condition", "key": "backtest_condition", "type": "Object" }, { "index_name": "backtest_result", "key": "backtest_result", "type": "STR" } ], "datas": [ { "strategy_basic_info": { "name": "SPY 200-day SMA Trend Following", "description": "Long-only daily trend following strategy on SPY that buys when the close price crosses above its 200-day simple moving average and exits to cash when the close crosses below the 200-day simple moving average, using close prices with no leverage or transaction costs." }, "backtest_condition": { "object": { "display_name": "Object", "type": "ticker", "ticker": [ "SPY" ] }, "open_signal": { "display_name": "Open Signal", "type": "criteria", "criteria": "Go long when SPY daily close price crosses above its 200-day simple moving average." }, "sell_signal": { "display_name": "Close Signal", "type": "criteria", "criteria": "Close the position when SPY daily close price crosses below its 200-day simple moving average." } }, "backtest_result": "https://cdn.ainvest.com/backtest/agent/session/784562/1800014825/424887cfa5b6ff0e20105949bb017405/spy_200dma_trend_backtest.json" } ] } } ``` --- ## 4. How to interpret these results ### 4.1 Return & growth profile - A **total return of ~232%** over ~16 years means capital grew by roughly a factor of **3.3×**. - An **annualized 8.0%** return is respectable and in line with long-run equity returns, but the key is **how** it achieves this: by **being out of the market in downtrends**. This kind of strategy tends to: - Participate in large, sustained bull markets (e.g., multi-year uptrends). - Step aside during severe bear phases, potentially avoiding the deepest drawdowns. ### 4.2 Risk & drawdown behavior - **Max drawdown 22.4%** is significantly milder than the kind of 40–50% drawdowns that broad equity markets have historically experienced in major crises. - Trend-following is doing its job: **cutting risk when prices roll over**. - However, 22% drawdown is still **material**; this is not a low-volatility or capital-preservation strategy. It's still an equity-like risk profile, just with **risk managed by the trend filter**. ### 4.3 Trade characteristics and edge - **Average trade +3.1%** with winners around **+8.6%** and losers around **−2.1%** suggests: - When the trend develops, the strategy can hold and accumulate meaningful profit. - Losing trades are cut relatively quickly (small magnitude). - For a trend follower, **payoff asymmetry** (big wins, small losses) is crucial; the statistics are consistent with that pattern. What this doesn’t tell us (but is typically important) without further breakdown: - **Win rate** (percentage of profitable trades). Trend followers often win **less than 50%** of the time but remain profitable because winners are much larger than losers. - **Concentration of return**: Often a small number of “big trends” drive a large part of the P&L. If you like, we can further decompose: - P&L by year or by regime (bull vs bear periods) - Distribution of trade returns - Average holding period per trade --- ## 5. Investment implications 1. **Role in a portfolio** - This strategy behaves like a **risk-managed equity exposure** rather than pure equity beta. - It’s particularly attractive for investors who are comfortable being sometimes **out of the market** to reduce drawdowns, in exchange for occasionally missing part of sharp V-shaped recoveries. 2. **When it tends to work well** - Strong, persistent trends (multi-month/multi-year bull markets). - Extended bear markets where staying out of the market avoids large losses. 3. **When it tends to struggle** - **Sideways or choppy markets** around the 200-day SMA, where repeated crosses generate **whipsaws** (small losses that add up). - Abrupt crashes that happen faster than the moving average can react; some part of the crash is usually still taken. 4. **Practical considerations** - Adding realistic **transaction costs** reduces performance, especially if the strategy trades frequently in choppy periods. - Choice of lookback window (e.g., 100 vs 200 vs 50 days) can significantly alter trade frequency and sensitivity. - Combining with other signals (e.g., volatility filters, breakout rules) can help reduce whipsaws. --- ## 6. Possible next steps Tell me how you’d like to refine this, for example: - **Different instrument:** e.g., QQQ, a single stock, sector ETF, BTC, FX pair. - **Different trend rule:** - Price vs 100‑day SMA - 50/200-day SMA golden cross - Donchian channel breakout (e.g., 55-day high/low) - **Risk overlays:** - Stop-loss (e.g., −10%) - Take-profit (e.g., +30%) - Max holding period (e.g., 250 trading days) - **Portfolio-level version:** same rule applied to multiple assets (requires a more customized, multi-asset backtest). If you tell me your **preferred market, time horizon, and risk tolerance**, I can tailor the trend-following strategy and re-run the backtest with parameters that match your investment profile.